from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-11 10:13:23.634515
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 11, Dec, 2020
Time: 10:13:27
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.5238
Nobs: 136.000 HQIC: -44.6680
Log likelihood: 1443.91 FPE: 1.82721e-20
AIC: -45.4513 Det(Omega_mle): 9.64855e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.460871 0.175939 2.619 0.009
L1.Burgenland 0.150690 0.085407 1.764 0.078
L1.Kärnten -0.292647 0.072042 -4.062 0.000
L1.Niederösterreich 0.120798 0.205363 0.588 0.556
L1.Oberösterreich 0.287698 0.170671 1.686 0.092
L1.Salzburg 0.166713 0.086609 1.925 0.054
L1.Steiermark 0.097563 0.122261 0.798 0.425
L1.Tirol 0.158778 0.081108 1.958 0.050
L1.Vorarlberg 0.005711 0.078448 0.073 0.942
L1.Wien -0.136183 0.163393 -0.833 0.405
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.527245 0.223468 2.359 0.018
L1.Burgenland 0.003451 0.108479 0.032 0.975
L1.Kärnten 0.335888 0.091503 3.671 0.000
L1.Niederösterreich 0.123615 0.260841 0.474 0.636
L1.Oberösterreich -0.193193 0.216776 -0.891 0.373
L1.Salzburg 0.196938 0.110006 1.790 0.073
L1.Steiermark 0.224901 0.155289 1.448 0.148
L1.Tirol 0.148490 0.103018 1.441 0.149
L1.Vorarlberg 0.202731 0.099640 2.035 0.042
L1.Wien -0.551468 0.207532 -2.657 0.008
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.309085 0.076513 4.040 0.000
L1.Burgenland 0.106758 0.037142 2.874 0.004
L1.Kärnten -0.018284 0.031330 -0.584 0.559
L1.Niederösterreich 0.125517 0.089309 1.405 0.160
L1.Oberösterreich 0.275928 0.074222 3.718 0.000
L1.Salzburg -0.008709 0.037665 -0.231 0.817
L1.Steiermark -0.041216 0.053169 -0.775 0.438
L1.Tirol 0.089444 0.035272 2.536 0.011
L1.Vorarlberg 0.131860 0.034116 3.865 0.000
L1.Wien 0.037157 0.071057 0.523 0.601
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.175208 0.088636 1.977 0.048
L1.Burgenland 0.002839 0.043027 0.066 0.947
L1.Kärnten 0.032963 0.036294 0.908 0.364
L1.Niederösterreich 0.056253 0.103459 0.544 0.587
L1.Oberösterreich 0.375549 0.085982 4.368 0.000
L1.Salzburg 0.089320 0.043632 2.047 0.041
L1.Steiermark 0.205805 0.061593 3.341 0.001
L1.Tirol 0.033907 0.040861 0.830 0.407
L1.Vorarlberg 0.108597 0.039521 2.748 0.006
L1.Wien -0.083255 0.082315 -1.011 0.312
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.642571 0.190727 3.369 0.001
L1.Burgenland 0.071594 0.092586 0.773 0.439
L1.Kärnten -0.010799 0.078097 -0.138 0.890
L1.Niederösterreich -0.078689 0.222625 -0.353 0.724
L1.Oberösterreich 0.113653 0.185016 0.614 0.539
L1.Salzburg 0.041868 0.093889 0.446 0.656
L1.Steiermark 0.118952 0.132537 0.897 0.369
L1.Tirol 0.235062 0.087925 2.673 0.008
L1.Vorarlberg 0.028457 0.085042 0.335 0.738
L1.Wien -0.143508 0.177126 -0.810 0.418
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.194981 0.131641 1.481 0.139
L1.Burgenland -0.043548 0.063903 -0.681 0.496
L1.Kärnten -0.011795 0.053903 -0.219 0.827
L1.Niederösterreich 0.178418 0.153656 1.161 0.246
L1.Oberösterreich 0.387387 0.127699 3.034 0.002
L1.Salzburg -0.029352 0.064802 -0.453 0.651
L1.Steiermark -0.040593 0.091478 -0.444 0.657
L1.Tirol 0.194772 0.060686 3.210 0.001
L1.Vorarlberg 0.039214 0.058696 0.668 0.504
L1.Wien 0.141122 0.122253 1.154 0.248
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.217427 0.167200 1.300 0.193
L1.Burgenland 0.072313 0.081165 0.891 0.373
L1.Kärnten -0.071905 0.068463 -1.050 0.294
L1.Niederösterreich -0.072291 0.195162 -0.370 0.711
L1.Oberösterreich -0.093754 0.162193 -0.578 0.563
L1.Salzburg 0.011071 0.082307 0.135 0.893
L1.Steiermark 0.386988 0.116188 3.331 0.001
L1.Tirol 0.526432 0.077079 6.830 0.000
L1.Vorarlberg 0.226475 0.074551 3.038 0.002
L1.Wien -0.198722 0.155277 -1.280 0.201
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.105055 0.193278 0.544 0.587
L1.Burgenland 0.033173 0.093824 0.354 0.724
L1.Kärnten -0.084120 0.079141 -1.063 0.288
L1.Niederösterreich 0.174131 0.225602 0.772 0.440
L1.Oberösterreich 0.035171 0.187490 0.188 0.851
L1.Salzburg 0.216111 0.095144 2.271 0.023
L1.Steiermark 0.173078 0.134310 1.289 0.198
L1.Tirol 0.066367 0.089101 0.745 0.456
L1.Vorarlberg 0.028870 0.086179 0.335 0.738
L1.Wien 0.263993 0.179495 1.471 0.141
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.595776 0.106707 5.583 0.000
L1.Burgenland -0.012065 0.051800 -0.233 0.816
L1.Kärnten 0.001483 0.043693 0.034 0.973
L1.Niederösterreich -0.041544 0.124553 -0.334 0.739
L1.Oberösterreich 0.285521 0.103512 2.758 0.006
L1.Salzburg 0.008464 0.052528 0.161 0.872
L1.Steiermark 0.018771 0.074152 0.253 0.800
L1.Tirol 0.070786 0.049192 1.439 0.150
L1.Vorarlberg 0.178189 0.047579 3.745 0.000
L1.Wien -0.098141 0.099098 -0.990 0.322
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.109001 -0.023230 0.188836 0.247901 0.034126 0.080576 -0.135785 0.136973
Kärnten 0.109001 1.000000 -0.052688 0.184722 0.108707 -0.153477 0.184509 0.013829 0.267881
Niederösterreich -0.023230 -0.052688 1.000000 0.246207 0.058727 0.188952 0.089001 0.028871 0.366788
Oberösterreich 0.188836 0.184722 0.246207 1.000000 0.258602 0.269190 0.078487 0.059549 0.057768
Salzburg 0.247901 0.108707 0.058727 0.258602 1.000000 0.134696 0.045289 0.083657 -0.049080
Steiermark 0.034126 -0.153477 0.188952 0.269190 0.134696 1.000000 0.084767 0.066459 -0.168018
Tirol 0.080576 0.184509 0.089001 0.078487 0.045289 0.084767 1.000000 0.130696 0.109556
Vorarlberg -0.135785 0.013829 0.028871 0.059549 0.083657 0.066459 0.130696 1.000000 0.061287
Wien 0.136973 0.267881 0.366788 0.057768 -0.049080 -0.168018 0.109556 0.061287 1.000000